Motion blur is a nuisance that can reduce both the aesthetic appeal and the utility of images captured when objects in the scene are not stationary. Likewise, motion blur can arise when the camera itself moves during exposure.
While motion de-blurring is a long-standing area of research, the field has broadened in recent years to include the capture and processing of moving scenes by computational cameras. Much of this recent work has addressed the issue that, even for linear, constant-velocity motion, traditional cameras fail to capture information about the world at certain spatial frequencies, resulting in a loss of texture.
In order to avoid such loss of texture, several acquisition techniques have been proposed to capture all spatial frequencies present in the scene. Unlike traditional cameras, these computational photographic methods capture visual information in a format that is not intended for immediate viewing. Instead, information about the scene is encoded in a photographic data structure from which a sharply-focused image can be derived.
Coded exposure is one such method, where the appearance of a moving object is encoded so that it can be derived by numerically stable de-convolution. In order to determine the parameters of that de-convolution, however, blur estimation must be performed.